Abstract
Record linkage refers to integrating data from heterogeneous sources to identify information regarding the same entity and provides the basis for sophisticated data mining. When privacy restrictions apply, the data sources may only have access to the merged records of the linkage process, comprising the problem of privacy preserving record linkage. As data are often dirty, and there are no common unique identifiers, the linkage process requires approximate matching and it renders to a very resource demanding task especially for large volumes of data. To speed up the linkage process, privacy preserving blocking and meta-blocking techniques are deployed. Such techniques derive groups of records that are more likely to match with each other. In this nectar paper, we summarize our contributions to privacy preserving blocking and meta-blocking.
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© 2015 Springer International Publishing Switzerland
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Karakasidis, A., Koloniari, G., Verykios, V.S. (2015). Privacy Preserving Blocking and Meta-Blocking. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_20
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DOI: https://doi.org/10.1007/978-3-319-23461-8_20
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